Causal AI and decision intelligence, in plain English.
Short, practical guides for the people who answer for the number: how causal AI differs from prediction and A/B testing, how to run a what-if analysis that holds up, and what to document for AI governance.
- No jargon without a definition
- Written for decision makers
- Free causal decision checklist
Causal AI vs predictive AI vs A/B testing: which one answers your question?
Predictive AI tells you what is likely to happen, an A/B test measures what happened when you changed something, and causal AI works out what would happen if you did, before you spend.
More guides
- Guide
Correlation vs causation in business decisions
Two numbers that move together are not evidence that changing one will move the other, and acting on that assumption is one of the most common ways to waste an intervention budget.
5 min read
Read the guide - Guide
What-if analysis for business decisions: beyond the spreadsheet scenario
A what-if analysis is only as good as the relationships behind it, and most spreadsheet scenarios assume those relationships instead of modeling them.
5 min read
Read the guide - Free checklist
The causal decision checklist: ten questions before you fund an intervention
Ten questions that take a team about an hour and routinely surface the assumption that would have sunk the intervention.
4 min read
Read the guide - Guide
AI model governance: what to document about the numbers your models produce
Governance expectations for AI and model risk differ by jurisdiction and sector, but they converge on one practical need: being able to say where a number came from and what was assumed.
5 min read
Read the guide - Comparison
Root cause analysis vs causal modeling: when the five whys are not enough
Root cause analysis traces the causes of one event back through a chain of whys. Causal modeling maps how the whole system behaves and answers what to change to prevent the next one.
5 min read
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